Relationship between heart rate variability parameters and inflammatory activity in patients with Crohn’s disease: a retrospective study

Study design and study population

This was a retrospective study, and was approved by Research Ethics Committee of Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences (NO.GDREC2015048H, Date: Jan 22, 2015). All patients with CD treated at our hospital from February 1, 2015 to February 1, 2020 were included. Patient demographic information, personal history, medical history including age, scope of CD involvement, disease behavior, course of disease, history of surgery, history of drug treatment, and the results of laboratory tests was collected and reviewed. The diagnosis of Crohn’s disease is based on the standards recommended by the World Health Organization (WHO). The extent of CD was classified using the Montreal classification. The Crohn’s disease activity index (CDAI) was calculated to evaluate the degree of active inflammation.

Written informed consent was obtained from all participants. The following exclusion criteria were applied: (1) Absence of medical history and/or examination records; (2) Diagnosis of heart conditions such as congenital heart defects, valvular dysfunction, conduction abnormalities, ischemic heart disease, or any form of heart failure; (3) Presence of endocrine disorders, including thyroid dysfunction, diabetes, neuropathies, or other systemic inflammatory conditions; (4) Use of medications that influence the cardiovascular system and nerve conduction, such as antidepressants, analgesics, or glucocorticoids; (5) A history of smoking or alcohol consumption; (6) Pregnancy.

Measurement of HRV

HRV measurements were performed by the same well-trained doctor. The examination was carried out in a fixed temperature and light controlled environment to avoid interference, and all examinations were performed within a same time period (10:00 am to 1:00 pm). Participants were instructed to avoid caffeine, strong tea, nicotine or other stimulants for 48 h before the examination, and to have adequate sleep the night before the examination. Patients were also instructed to fast for at least 2 h before the examination, and rest for at least 15 min before the examination.

A short-range HRV analyzer (DLP6000; General Meditech Inc., Shenzhen, Guangdong, China) was used to record and evaluate patient sitting ECG signals, and an electrocardiogram was continuously recorded for 5 min using three leads of the standard limb lead II. During the examination, patients were required to be completely relaxed, breathe regularly, and to not talk or move. Technicians carefully observed the patients to ensure there was no subjective discomfort or motion artifacts during the recording. The device’s software (version 1.0.5649.19505) processed the data, and heart rate variability (HRV) was evaluated using both time-domain and frequency-domain approaches. Autonomic nervous disorders (vagus nerve or sympathetic nerve) related to specific diseases can be reflected in HRV indexes. Time-domain and frequency-domain HRV parameters calculated in this study are shown in Table 1.

Statistical analysis

Continuous variables that followed a normal distribution were presented as means ± standard deviations (SD). For those that were not normally distributed, data were expressed as medians with interquartile ranges, while categorical variables were displayed as frequencies and percentages. To compare differences between groups, one-way ANOVA was employed for normally distributed variables, the Kruskal-Wallis test for non-normally distributed variables, and the chi-square test for categorical variables. Univariate linear regression analysis was conducted to examine the relationships between HRV parameters and the CDAI. In accordance with the STROBE guidelines, we reported results from unadjusted, minimally adjusted, and fully adjusted models. Covariates, once incorporated into the model, resulted in at least a 10% change in the matched odds ratio and were thus adjusted (Kernan et al., 2000). Furthermore, the generalized additive model (GAM) was applied to capture potential non-linear relationships. If a non-linear correlation was identified, a two-piecewise linear regression model was utilized to assess the threshold effects of HRV parameters on CDAI, following a smooth curve. When a clear relationship between HRV parameters and CDAI appeared in the smooth curve, a recursive method was employed to determine the inflection point, optimizing the model’s likelihood (Liu et al., 2013). All statistical analyses were performed using R software packages (http://www.r-project.org, R Foundation) and EmpowerStats (http://www.empowerstats.com, X&Y Solutions, Inc., Boston, MA). A P-value of less than 0.05 (two-sided) was considered indicative of statistical significance.

Patients

Of the 92 patients with CD (68 males, 24 females) identified in the medical records, 10 were excluded: three (two males, one female) with incomplete medical histories, two males with arrhythmia, four (three males, one female) who had received glucocorticoids, antidepressants, and analgesics, and one male smoker. Consequently, 82 participants (60 males, 22 females) were enrolled in the study. (Fig. 1).

Patient characteristics

Patient characteristics are summarized in Table 2. The mean age of the patients was 28.84 ± 11.87 years old, and approximately 73.2% of them were male. The patients were divided into two groups based on CDAI: active CD, CDAI > 150 (mean 248.81 ± 83.27) and remission CD, CDAI ≤ 150 (mean 94.43 ± 33.24). No substantial variations were observed between the two groups in terms of age, gender, marital status, educational attainment, Montreal classification (including age at diagnosis, disease site, and disease behavior), disease duration, history of bowel resection, albumin levels, or white blood cell (WBC) count. However, a significantly greater number of patients in the remission group received biologics for treatment. Compared with remission group, the body mass index (BMI) and HRV parameters (including TP, LF, HF, SDNN) in active group were significantly lower, while heart rate (HR) and CRP level were significantly higher.

Univariate analysis

The results of univariate analysis are shown in Table 3. Univariate analysis showed that BMI, HRV parameters, HR, CRP, WBC, and medications were related to the CDAI. However, gender, age, age at diagnosis, disease location, disease behavior, level of education, marital status, history of bowel resection, duration of disease, and albumin were not related to the CDAI.

Relations between HRV indices and CDAI

The relationship between HRV parameters and CDAI was assessed using a univariate linear regression model, with both unadjusted and adjusted model results presented in Table 4. In the unadjusted model, HRV parameters including TP, HF, and SDNN were negatively correlated with CDAI (β = −0.02, 95% confidence interval [CI] [−0.03 to −0.01], P = 0.0025; β = −0.04, 95% CI [−0.07 to −0.01], P = 0.0047; β = −1.86, 95% CI [−2.80 to −0.92], P = 0.0002, respectively). In the minimally adjusted model (adjusted for age and gender), the results did not change significantly (β = −0.02, 95% CI [−0.03 to −0.01], P = 0.0029; β = −0.04, 95% CI [−0.07 to −0.01], P = 0.0065; β = −2.02, 95% CI [−3.03 to −1.02], P = 0.0002, respectively). However, in the fully adjusted model (adjusted for age, gender, medications, BMI, history of bowel resection, and disease location), the negative correlations between TP, HF, and SDNN and CDAI were not significant (β = −0.01, 95% CI [−0.02 to 0.01], P = 0.3465; β = −0.01, 95% CI [−0.04 to 0.01], P = 0.3616; β = −0.94, 95% CI [−1.88 to 0.00], P = 0.0529).

In the unadjusted model, the negative correlation between HRV parameters, LF, and CDAI was not significant (β = −0.03, 95% CI [−0.06 to −0.00], P = 0.0645). There was no change in the minimally adjusted model (β = −0.03, 95% CI [−0.06 to −0.00], P = 0.0837). In the fully adjusted model, the negative correlation between LF and CDAI was still not significant (β = −0.00, 95% CI [−0.03 to 0.02], P = 0.7983).

For the trend test, TP, LF, HF, and SDNN were used as categorical variables (tertiles). The results showed that as the value of TP and SDNN increased the value of the CDAI decreased, and the negative correlation trends were significant (P = 0.0306 and P = 0.0101).

Analyses of non-linear relations

Since HRV parameters are continuous variables, it was necessary to perform non-linear relations analyses. The results showed that after adjusting for age, gender, BMI, disease location, history of bowel resection, and medications there were non-linear relations between TP, LF, SDNN, and the CDAI, while there was a linear relation between HF and the CDAI (Fig. 2).

A two-piecewise linear regression model was employed to determine the inflection points of the curves (as shown in Table 5). For TP, the inflection point was observed at 246.68 ms², with an effect value of −0.77 on the left side of the point (95% CI [−1.15 to −0.4], P = 0.0001). For LF, the inflection occurred at 60.74 ms², where the effect value on the left side was −3.95(95% CI [−5.63 to −2.27], P = < 0.0001). For SDNN, the inflection point was at 28.63 ms, and the effect value to the left of this point was −6.74 (95% CI [−9.88 to −3.59], P < 0.0001). Notably, no correlation was found between TP, LF, SDNN, and the CDAI on the right side of the respective inflection points (β = −0.00, 95% CI [−0.01 to 0.01], P = 0.9523; β = 0.01, 95% CI [−0.02 to 0.03], P = 0.5170; β = 0.46, 95% CI [−0.67 to 1.59], P = 0.4237).